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dc.contributor.advisorAnderson, David
dc.contributor.authorFair, Kaitlin Lindsay
dc.date.accessioned2018-05-31T18:08:50Z
dc.date.available2018-05-31T18:08:50Z
dc.date.created2017-05
dc.date.issued2017-04-05
dc.date.submittedMay 2017
dc.identifier.urihttp://hdl.handle.net/1853/59782
dc.description.abstractWe develop a novel design methodology to map the biologically plausible Locally Competitive Algorithm (LCA) to the brain-inspired TrueNorth chip to solve for the sparse approximation of a signal, offering the largest LCA dictionaries implemented on neuromorphic hardware to date with perfect precision. We observe low-power consumption in the operation of the LCA on the TrueNorth chip. We also explain methods to map other sparsity-based probabilistic inference problems onto the hardware using our design methodology. We describe the optimal way to achieve high-precision calculations by encoding and decoding signals within time windows. We discuss in detail functional processing units for use on the hardware that offer non-linear thresholds, increased vector-matrix multiplication precision, and the ability to accurately implement a recurrent network on the TrueNorth chip. Our design methodology offers the foundation for low-power embedded systems signal processing applications using the TrueNorth chip.
dc.format.mimetypeapplication/pdf
dc.language.isoen_US
dc.publisherGeorgia Institute of Technology
dc.subjectNeuromorphic
dc.subjectBio-inspired
dc.subjectTrueNorth
dc.subjectSparsity
dc.subjectSparse approximation
dc.titleA biologically plausible sparse approximation solver on neuromorphic hardware
dc.typeDissertation
dc.description.degreePh.D.
dc.contributor.departmentElectrical and Computer Engineering
thesis.degree.levelDoctoral
dc.contributor.committeeMemberRomberg, Justin
dc.contributor.committeeMemberRozell, Christopher
dc.contributor.committeeMemberDavenport, Mark
dc.contributor.committeeMemberAndreou, Andreas
dc.date.updated2018-05-31T18:08:50Z


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